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Patient (Persona) Analytics: A Catalyst to Drive Engagement and Compliance for Risk based Outcomes Contracting

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This presentation throws light on:
• Leveraging actionable insights from next generation analytics to drive outcomes based programs and risk-based contracting
• Understanding how to use personas and analytics to improve compliance and effectiveness
• Use the science behind the data to focus targeted outreach to the right populations and providers
For more information on our analytics solutions, please visit: http://www.sciohealthanalytics.com/offerings/solutions/care-optimization

Published in: Healthcare
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Patient (Persona) Analytics: A Catalyst to Drive Engagement and Compliance for Risk based Outcomes Contracting

  1. 1. ©2016 SCIO Health Analytics®. Confidential and Proprietary. All rights reserved. |1 Once We Understand, Change Results. Patient (Persona) Analytics A Catalyst to Driving Engagement and Compliance for Risk based Outcomes Contracting October 5, 2016
  2. 2. |8 MARKET DRIVERS DRUG PRICING PRESSURE MARKET DRIVERS COMPLEXITY OF HEALTHCARE VALUE-BASED CARE MARKET STRATEGIES NEW BUSINESS MODELS PAYVIDERS OUTCOMES BASED CONTRACTING COLLABORATION RISK & DATA SHARING FOR BETTER PATIENT OUTCOMES PATIENT ENGAGEMENT & CONSUMERISM INSIGHTS & TECHNOLOGY AS A KEY ENABLER
  3. 3. |9 Potential Headwinds For Life Sciences HEALTH PLANS WILL REQUIRE OUTCOMES BASED RISK STRATEGY SPECIALTY DRUG COST IMPACT ON OVERALL HEALTH CARE SPEND IMPACT OF MEDICATION COMPLIANCE ON TOTAL COST OF CARE IMPACT ON THE GROWTH OF MANAGED MEDICARE AND MEDICAID DISPARATE AND SILOED DATA PATIENT BEHAVIOR IS CHANGING- CONSUMER AS THE NEW MONEY MANAGER PAYER“ ARE REQUIRING MORE RI“K ON DRUG IMPACT AND EVIDENCE OF PATIENT “UCCE““
  4. 4. |11 360o ANALYSIS MATTERS Singular View SCIO Comprehensive view Vs. IMPACT FOCUS Measure and predict areas for greatest impact and identification of untreated patients. RESOURCE FOCUS Focus resources on contracting payer collaboration and commercial operation resources 3600 FOCUS Incorporate multiple data sources to determine behaviors leading to compliance, engagement and risk reduction. SCIO believes in bringing insights to light through a comprehensive lens
  5. 5. |13 Patient Approach to Value: Patient Persona’s PROVIDER 360° ANALYSIS Prescriptive Analytics Predictive Analytics OUTCOMES DataFlow Dynamic Risk Management of Patient Populations Provider Performance Management Treatment Pathways Individualized for each Persona Patient Migration Towards Greater Compliance
  6. 6. |14 72% 14% 4% 1% $200K+ $100K - $200K $50K - $100K Less than $50K Estimated Income Patient Persona example: High Utilizers 23% 58% 12% 7% 65+ 55-64 35-54 16-34 Age Group Description High-risk adults, mostly non- college level education, blue collar employees with average income. They happen to be high utilizers of healthcare services given that they are higher risk and the average chronic conditions is greater than one. Intervention: High risk with high utilization, so need to education HCP to ensure steerage Demographic Attributes % Above Poverty Level 68% % Blue Collar Employed 36% % Single Family Dwelling 33% % Married 48% % Household with children 60% Utilization Attributes IP Utilization 1.45 ER Utilization 0.84 # Average Chronic Conditions 2.05 Paid Amount PMPM ER $95 Paid Amount PMPM IP $247 Gender Education School College Individuals with Income Level > $50K Median Age 43 $146K 72% HCP Engagement Average Spending Median Home Value Socio-Economic Score 62 Spending Pattern 60 100 Low Risk Median Risk Prospective Score 0.71
  7. 7. |15 Healthy & Affluent Balanced Adults High Utilizers Quality Driven Cost Conscious Chronic older Adults High Cost Baby Boomers Example of Personas – Clinical Attributes No.of chronic conditions ER Paid PMPM IP Paid PMPM ER Utilization IP Utilization 0.54 0.70 0.71 0.86 0.82 1.02 1.13 Median Risk Prospective Score 0.6 0.7 0.8 1.2 1.2 1.3 1.6 0.09 0.05 0.10 0.04 0.07 0.08 0.09 0.25 0.22 0.34 0.23 0.18 0.21 0.23 $75 $73 $147 $54 $75 $118 $248 $10 $9 $14 $9 $7 $10 $11
  8. 8. |18 THE OPIOID CHALLENGE
  9. 9. |19 • Market Potential is up to 6x the current number of patients on Opioid Dependence therapy. 80% of which are non compliant. Patients are stratified by compliance and prospective risk (10) Highest prospective risk patients (e.g. decile 10) consume 75% of healthcare spend Each year prospective risks shifts patients between each decile group (e.g. patients health improves resulting in lower risk while other patients become more sick) Existing and Untreated Patients by Opioid Dependence Compliance and Risk
  10. 10. |20 Example of Prospective Risk by CBSA (Core Based Statistical Area) Top 5 CBSAs % of US Opioid Dependence Rx New York 4.7% Philadelphia 3.7% Pittsburgh 3.6% Boston 2.6% Detroit 2.3% High=top 20% Middle=middle 30% Bottom=bottom 50%
  11. 11. |21 UNDIAGNOSED DIABETES PATIENT CHALLENGE
  12. 12. |22 Patients are stratified by compliance and prospective risk (10) Highest prospective risk patients (e.g. decile 10) consume 75% of healthcare spend Each year prospective risks shifts patients between each decile group (e.g. patients health improves resulting in lower risk while other patients become more sick) Patients by Diabetes Groups, Compliance and Risk
  13. 13. |23 Patients at Risk for Diabetes by Risk Category - 20 40 60 80 100 120 140 10 9 8 7 6 5 4 3 2 1 Hospitalizationsper1000 Risk Decile Hospitalizations per 1000 - 20 40 60 80 100 120 140 160 180 200 10 9 8 7 6 5 4 3 2 1 ERVisitsper1000 Risk Decile ER Visits per 1000 10 – High Risk…..1 – Low Risk Undiagnosed Diabetic Patients Prospective Risk: 9 ER/1000: 15 IP/1000: 24 Impactability: 7 Avg. #CC: 2.67 Rank across all Counties
  14. 14. |24 Identify Gap Prioritization at the Patient Level Patient Risk Score Impactability Score Gap1 Gap2 Gap3 000000010506 0.89 1.68 Diabetes - Consider Foot Exam HbA1c Less Than 7 Target 000000010331 0.83 1.51 Lipid Panel Spirometry 000000010043 0.81 1.64 Consider Pulmonary Rehabilitation AST Test Physical Therapy 000000010154 0.73 1.39 Lipid Panel Spirometry Alpha-Glucosidase 000000010539 0.73 1.04 Diabetes and Macroalbuminuria - Consider Adding an ACE Inhibitor or ARB Diabetics 50 years and Older - Consider Screening for Peripheral Arterial Disease Patient In Last 12 Months Cost Incurred in Last 12 Months Probability of ER Admission Predicted Probability of ER Admit IF all the gaps are closed Difference Impactability Score# Hospitalization # ER Visits InPatient (PMPM) ER (PMPM) OutPatient (PMPM) Professional (PMPM) Pharmacy (PMPM) 000000010506 1 1 $2,999 $302 $209 $201 $130 93% 22% 71% 1.68 000000010331 0 0 $237 $158 $147 90% 27% 64% 1.51 000000010043 0 2 $287 $231 $225 $133 91% 22% 69% 1.64 000000010154 0 0 $231 $178 $103 74% 16% 58% 1.39 000000010539 0 0 $340 $181 $96 70% 27% 44% 1.04 000000010507 0 0 $333 $208 $134 73% 24% 49% 1.15
  15. 15. |25 Utilize Benchmarking to Identify Patterns of Potential Utilization and Waste Drill into High Risk Patients to Uncover Impactability Score Measure Impact of Non Compliance on HEDIS Star Ratings for Managed Medicare and Medicaid Plans Using Specific Products Identify Geographies at Highest Risk Across Commercial, Managed Medicare and Medicaid Ar ed With A “i gle “heet of Music Part er with Payers on Risk Based Outcomes Ide tify the Geographic Hot “pots a d Draft Care Plan Messages for HCPS and Associated Health Plans Understand the Compliant / Non-compliant Patient Persona Build Capabilities to Measure Outcomes and Pay for Value Evaluate Impact on Hospital Readmissions and Bundled Payments Track Results with Providers and Health Plan Care Managers Track Utilization of Medication on Impact to Other High Cost Chronic Conditions Which Comorbidity Increases Patient Prospective Risk Summary: Persona Analytics Enabled Response to Headwinds

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